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<p>In this study, the inverse heat conduction problem (IHCP) is examined on a two-dimensional (2D) cross section of a horizontal pipe. The objective is to estimate the time-dependent fluid temperature near the inner wall of the pipe and determine the convective heat transfer coefficient between the inner wall and the fluid. In the field of industrial fluid transportation, temperature fluctuation occurs when hot and cold fluids are not sufficiently mixed in the pipeline, and a thermal delamination phenomenon forms. Temperature fluctuations can cause thermal delamination and thermal stripping, resulting in periodic variations in thermal stress that can ultimately lead to thermal fatigue in pipeline structures. To address this, this paper proposes an inversion algorithm utilizing the ant lion optimizer–dual adaptive salp swarm algorithm (ALO–DASSA). The accuracy of the inversion algorithm is verified by comparing with the experimental values of the outer wall temperature. The experimental results enrich the theoretical system in the field of nuclear pipeline safety monitoring and also provide a useful reference for practical engineering applications.</p>

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A study of multiparameter synchronized inversion for nuclear power plants based on ant lion optimizer–double adaptive salp swarm algorithm

  • 期刊论文

  • 发表日期：

    2025年9月9日

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  • Shoubin Wang,
  • Wenhao Sun,
  • Jie Song,
  • Baohua Cheng,
  • Yuan Zhou,
  • Xuanman Lv,
  • Guili Peng

摘要

In this study, the inverse heat conduction problem (IHCP) is examined on a two-dimensional (2D) cross section of a horizontal pipe. The objective is to estimate the time-dependent fluid temperature near the inner wall of the pipe and determine the convective heat transfer coefficient between the inner wall and the fluid. In the field of industrial fluid transportation, temperature fluctuation occurs when hot and cold fluids are not sufficiently mixed in the pipeline, and a thermal delamination phenomenon forms. Temperature fluctuations can cause thermal delamination and thermal stripping, resulting in periodic variations in thermal stress that can ultimately lead to thermal fatigue in pipeline structures. To address this, this paper proposes an inversion algorithm utilizing the ant lion optimizer–dual adaptive salp swarm algorithm (ALO–DASSA). The accuracy of the inversion algorithm is verified by comparing with the experimental values of the outer wall temperature. The experimental results enrich the theoretical system in the field of nuclear pipeline safety monitoring and also provide a useful reference for practical engineering applications.